STAT 468
Design of Experiments
Winter 2026
Topics
- Experimental design and analysis of variance
- Single-factor experiments
- Randomized blocks and Latin squares
- Factorial designs
- Two-level factorial designs
- Blocking and confounding
- Fractional factorial designs
- Additional factorial-design analysis
- Data analysis in R
Readings
- W. John Braun and Duncan J. Murdoch. A First Course in Statistical Programming with R, 2nd edition. Cambridge University Press.
- Listed textbook (requirement not stated): Douglas C. Montgomery. Design and Analysis of Experiments, 10th edition. John Wiley, 2020.
- Reference (not explicitly required): S. R. Searle. Linear Models. John Wiley, 1971.
Winter 2024 · STAT 468
Readings
- Design and Analysis of Experiments, 10th Edition, by Douglas C. Montgomery (John Wiley, 2020).
- Linear Models by S.R. Searle (John Wiley, 1971).
Winter 2022 · STAT 468
Readings
- Design and Analysis of Experiments, 10th Edition, by Douglas C. Montgomery (John Wiley, 2020).
- Linear Models by S.R. Searle (John Wiley, 1971).
Sources
- Course catalogue ↗2026–2027
- Syllabus or course outline ↗Winter 2026
- Historical course outline ↗Winter 2024
- Historical course outline ↗Winter 2022
- Winter 2026 outline. Course header explicitly lists STAT 468, MAST 679, MAST 881. Braun and Murdoch is identified as a useful reference for R. Additional graduate self-study readings are mentioned without bibliographic details.
- Historical source. Generic Textbook/References headings remain requirement-unspecified. Bibliographic editions and years are retained as printed; source inconsistencies may remain. Automated extraction with conservative filtering; distinct bibliography strings screened for false positives; entire syllabus not manually reviewed.
- Historical source. Generic Textbook/References headings remain requirement-unspecified. Bibliographic editions and years are retained as printed; source inconsistencies may remain. Automated extraction with conservative filtering; distinct bibliography strings screened for false positives; entire syllabus not manually reviewed.